• Science and humanities are increasingly data-driven
  • Early-career training has not prepared all researchers for this

• Enable systematic, replicable and reproducible work
  • Design principles
    • Best practices for data
  • Software development methods
    • Automation of repetitive calculations

• What a computer does
  • A series of instructions
  • Data is piped through programs, and a result emerges

• What a researcher does
  • Exploring data, developing hypotheses, writing code, interpreting results
• Outputs include:
  • datasets, methods, teaching materials, software, papers, etc.

• Reproduced from Stoudt et al. (2021)

• Fundamentals of R and RStudio
• Fundamentals of programming (in R)
• Data management with the tidyverse
• Publication-quality data visualisation with ggplot2
• Reporting with RMarkdown

• R is:
  • a programming language
  • the software that interprets/runs programs written in the R language

• free (though commercial support can be bought)
• widely used
  • sciences, humanities, engineering, statistics, etc.
• has many excellent specialised packages for data analysis and visualisation
• international, friendly user community

• RStudio is an integrated development environment (IDE)

• Separates data from analysis
• Not point-and-click: every step is explicit and transparent
• Easy to share, adapt, reuse, publish analyses with new/modified data (GitHub)
• R can be run on supercomputers, with extremely large datasets…

• An item (object) of data goes in the box (which is called Name)
• When we refer to the box (variable) by its name, we really mean what’s in the box

x <- 1 / 40
x

## [1] 0.025

x ^ 2

## [1] 0.000625

log(x)

## [1] -3.688879

name <- "Samia"
name

## [1] "Samia"

current_temperature = 28.6
subjectID = "GCF_00001236452.1"
GPS_Location = "54N, 36E"

• descriptive, but not too long
• letters, numbers, underscores, and periods ([a-zA-z0-9_.])
• cannot contain whitespace or start with a number (x2 is allowed, 2x is not)
• case sensitive (Weight is not the same as weight)
• do not reuse names of built-in functions
• Consistent style:
  • lower_snake, UPPER_SNAKE, lowerCamelCase, UpperCamelCase

• automate complicated tasks
• make code more readable and reusable

• Some functions are built-in (in base packages, e.g. sqrt(), lm(), plot())
• Groups of related functions can be imported as libraries

args(fname)            # arguments for fname
?fname                 # help page for fname
help(fname)            # help page for fname
??fname                # any mention of fname
help.search("text")    # any mention of "text"
vignette(fname)        # worked examples for fname
vignette()             # show all available vignettes

mass <- 47.5
age <- 122
mass <- mass * 2.3
age <- age - 20

• mass = 47.5, age = 102
• mass = 109.25, age = 102
• mass = 47.5, age = 122
• mass = 109.25, age = 122

• Use a single working directory per project/analysis
  • easier to move, share, and find files
  • use relative paths to locate files
• Treat raw data as read-only
  • keep in a separate subfolder (data?)
• Clean data ready for work programmatically
  • keep cleaned/modified data in separate folder (clean_data?)
• Consider output generated by analysis to be disposable
  • can be regenerated by running analysis/code

• RStudio tries to help you manage your projects
  • R Project concept - files and subdirectory structure
  • integration with version control
  • switching between multiple projects within RStudio
  • stores project history

• At the console (you’ve done this)
• In a script
• As an interactive notebook
• As a markdown file
• As a Shiny app

We’re going to create a new dataset and R script.

• Putting code in a script makes it easier to modify, share and run

• Patients have been given a new treatment for arthritis
• We have measurements of inflammation over a period of days for each patient
• We want to produce a preliminary analysis and graphs for this data

• https://github.com/swcarpentry/r-novice-inflammation/raw/main/data/r-novice-inflammation-data.zip

(the link is also available on the course Etherpad page)

• You created data manually earlier, but this is rare
• Data are most commonly read in from plain text files

read.csv(file = "data/inflammation-01.csv", header = FALSE)

Someone gives you a data file that has:

• a comma (,) as the decimal point character
• semi-colon (;) as the field separator

How would you open it, using read.csv()

• We use indexing to refer to elements of a matrix
  • square brackets: []
  • row, then column: [row, column]

data[1, 1]     # First value in dataset
data[30, 20]   # Middle value of dataset

• To get a range of values, use the : separator (meaning ‘to’)

data[1:4, 1:4]   # rows 1 to 4; columns 1 to 4

• To select a complete row or column, leave it blank

data[5, ]     # row 5
data[, 16]    # column 16

• R provides useful functions to summarise data
• We can use indexing to get summary information on individual patients and days

max(data)           # largest value in dataset
max(data[2, ])      # largest value for row (patient) 2
min(data[, 7])      # smallest value on column (day) 7
mean(data[, 7])     # mean value on day 7
sd(data[, 7])       # standard deviation of values on day 7

• Calculating for every patient (or day) this way is tedious

• So apply a function (mean) to each row in the data:

• R has several ways to automate this process

apply(X = data, MARGIN = 1, FUN = mean)

rowMeans(data)
colMeans(data)

“The purpose of computing is insight, not numbers.” - Richard Hamming

• Base graphics are powerful tools for visualisation and understanding

plot(avg_inflammation_patient)

max_day_inflammation <- apply(dat, 2, max)
plot(max_day_inflammation)

plot(apply(dat,2,min))       # 3 functions in one!

Can you add plots to your script showing:

• scatterplot of standard deviation of inflammation across all patients, by day
• a histogram of average inflammation across all patients, by day

• Basic data types in R
• Common data structures in R
• How to find out the type/structure of R data
• Understand how R’s data types and structures relate to your own data

• R is mostly used for data analysis
• R has special types and structures to help you work with data
• Much of the focus is on tabular data (data frames)

• Data types in R are atomic
  • All data structures are built from these

Create examples of data with the following characteristics:

For each variable, test that it has the data type you intended

• vector
• factor
• list
• data.frame

Vectors are atomic: they can contain only a single data type

What data type are the following vectors (xx, yy, zz)?

xx <- c(1.7, "a")
yy <- c(TRUE, 2)
zz <- c("a", TRUE)

• Coercion means changing data from one type to another
• R will perform implicit coercion on vectors to make them atomic

• Manual coercion with as.<type_name>()

Data comes as one of two types:

• quantitative: e.g. integers or real numbers
  (weight <- 17.2; rooms <- 7)
• categorical: e.g. ordered or unordered classes
  (grade <- "8", coat <- "brindled")

• Factors are special vectors that represent categorical data
  • Stored as vectors of labelled integers
  • Cannot be treated as strings/text

Create a new factor, defining control and case experiments, and inspect the result:

f <- factor(c("case", "control", "case", "control", "case"))
str(f)

##  Factor w/ 2 levels "case","control": 1 2 1 2 1

• lists are like vectors, but can hold any combination of datatype
  • elements in a list are denoted by [[]] and can be named

# create a list
l <- list(1, 'a', TRUE, matrix(0, nrow = 2, ncol = 2), f)
l_named <- list(a = "SWC", b = 1:4)

• We have used indexing, slicing and names to get data by ‘location’

> animal[c(2,4,6)]
[1] "o" "k" "y"
> l_named$b
[1] 1 2 3 4

• Logical indexes select data that meets certain criteria

x <- c(5.4, 6.2, 7.1, 4.8, 7.5)
mask <- c(TRUE, FALSE, TRUE, FALSE, TRUE)
x[mask]
x[x > 7]